Dynamic sampling in sports equipment

ABSTRACT

Analysis of sporting equipment characteristics may be analyzed using dynamic sampling rates. For example, analyzing a golf swing may include the use of one or more sensors providing data at various sampling rates. According to some aspects, the sampling rate may be dynamically modified upon determination of one or more golf equipment characteristics, environmental conditions, player characteristics and the like. In one example, a sampling rate processor may dynamically select a sampling rate at which data is sampled from one or more sensors. In some examples, by dynamically selecting the sampling rate, an analysis may be tailored to various types, and portions of a golf swing, in addition to producing power consumption by the analysis instruments in the golf club system. According to other aspects, triggering conditions for modifying a sampling rate may be determined from a population of one or more previous golf swings performed by a user.

RELATED APPLICATION INFORMATION

This application is a continuation of U.S. application Ser. No.15/153,463, filed May 12, 2016, which is a divisional of U.S.application Ser. No. 13/907,776 filed May 31, 2013, issued as U.S. Pat.No. 9,342,737, the entire contents of which are incorporated herein byreference in their entirety for any and all non-limiting purposes.

TECHNICAL FIELD

Aspects of the disclosure relates to sporting equipment. Moreparticularly, aspects described herein include dynamic sampling ofsensor data for various types of sporting equipment.

BACKGROUND

Sports such as golf are enjoyed by a wide variety of players—players ofdifferent genders and dramatically different ages and/or skill levels.In golf, players at all skill levels seek to improve their performance,lower their golf scores, and reach that next performance “level.”Manufacturers of all types of golf equipment have responded to thesedemands, and in recent years, the industry has witnessed dramaticchanges and improvements in golf equipment. For example, a wide range ofdifferent golf ball models now are available, with balls designed tocomplement specific swing speeds and/or other player characteristics orpreferences, e.g., with some balls designed to fly farther and/orstraighter; some designed to provide higher or flatter trajectories;some designed to provide more spin, control, and/or feel (particularlyaround the greens); some designed for faster or slower swing speeds;etc. A host of swing and/or teaching aids also are available on themarket that promise to help lower one's golf scores.

Being the sole instrument that sets a golf ball in motion during play,golf clubs also have been the subject of much technological research andadvancement in recent years. For example, the market has seen dramaticchanges and improvements in putter designs, golf club head designs,shafts, and grips in recent years. Additionally, other technologicaladvancements have been made in an effort to better match the variouselements and/or characteristics of the golf club and characteristics ofa golf ball to a particular user's swing features or characteristics(e.g., club fitting technology, ball launch angle measurementtechnology, ball spin rates, etc.).

Improvement in golf may also be achieved by studying a player's swingand adjusting his or her posture and swing characteristics to maximizemomentum, head speed, lie angle, impact location and the like. However,it may be difficult for a user to independently to determine head speedor an impact location of the golf ball against the golf club face.Additionally, having additional information regarding how (e.g., where)a golfer is hitting a golf ball with a golf club may allow the golfer tobetter improve his or her swing. Several factors affect a golfer'sswing. For example, the lie angle, the loft angle, type of golf ball,and the club head angle of the club during impact with a golf ballgreatly affect the trajectory of the ball.

Various analysis systems have been developed for analysis andcommunication of various golf swing performance metrics to a player.However, due to the inherent complexity of a golf swing, complex, andcomputationally-intensive processes may be required for analyzing aplayer's golf swing, wherein these processes may receive informationrelated to one or more characteristics of a golf swing from one or moresensors. Analysis systems have been designed, in some instances, to beportable, and integrated into golf club structures such that analysis ofa player's golf swing may be performed on a golf course during a roundof golf. These portable analysis systems may consume power from portablepower supplies, such as batteries, which are carried by a player duringthe round of golf. Accordingly, systems, methods, computer-readablemedia storing computer-executable instructions that will reduce thecosts and improve the efficiencies and power consumption of suchanalysis systems would be a welcome advance in the art.

BRIEF SUMMARY

One or more of the above-mentioned needs in the art are satisfied byaspects described herein. According to one aspect, a golf club may beself contained and include sensors and transmitters located therewithin.As a result, the golf club can be used during a round of golf to analyzea golfer's characteristics without interfering with the golfer. In someembodiments, the golf club may wirelessly transmit golf swingcharacteristic data to a portable device, such as a personal digitalassistant (PDA) or watch.

Aspects described herein relate to non-transitory machine-readable media(e.g., computer-readable media) with executable instructions forreceiving golf swing data into a sampling rate processor on a golf club.The golf swing data may be received from a sensor on the golf club,wherein an analysis processor samples data from the sensor at varioussampling rate. In one example, the processor may sample data from thesensor at a first sampling rate to classify a current movement of thegolf club into one of a plurality of golf swing categories. Theprocessor may further sample data at a second sampling rate based on theclassified golf swing category. For example, different sampling ratesmay be used for different types of golf swing categories orclassifications.

In another aspect, data may be received and/or sampled from a sensor ona golf club at a first sampling rate. The analysis processor maysubsequently identify one or more golf swing characteristics from thereceived data, and compare one or golf swing characteristics to a storedgolf swing sample, threshold or predefined rule. Subsequently, asampling rate processor may select a second sampling rate at which theanalysis processor is to sample the sensor data, based on the comparisonof the received data to a stored golf swing sample.

In yet another aspect, a golf club may be a self-contained instrumentedgolf club with a golf club head, a shaft, and a sensor for capturingdata related to one or more metrics of a golf swing. The instrumentedgolf club further includes a sampling rate processor, which isconfigured to select a sampling rate at which the sensor is to capturegolf swing data.

According to further aspects, a golf club or a golf swing analysisdevice may dynamically modify a sampling rate of one or more sensors inresponse to certain triggers. For example, triggers may relate to anamount of time since a swing was initiated (e.g., back swing or foreswing), detecting a threshold velocity or acceleration of the club head,detecting a change in direction of acceleration of the club head and thelike and/or combinations thereof. In some arrangements, the triggers(e.g., trigger times, threshold velocity or acceleration, etc.) may bedetermined based on a population of one or more sample swings orpractice swings by a golfer.

The various aspects described herein may further be applied to a varietyof sporting equipment types and for a variety of sport equipmentcharacteristics and metrics.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. The Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 illustrates an example system and environment in which variousaspects of this disclosure may be used and implemented.

FIG. 2 is a schematic block diagram of a sensor device.

FIG. 3 is a schematic block diagram of an exemplary embodiment of aninstrumented golf club.

FIG. 4 is a schematic block diagram of an alternative implementation ofan instrumented golf club.

FIG. 5 depicts one implementation of an instrumented golf club with agolf club head configured with electromagnetic sensors.

FIG. 6 depicts another implementation of an instrumented golf club witha golf club head configured with magnetic field sensors.

FIG. 7 schematically depicts a swing path determination process.

FIG. 8 depicts an implementation of an instrumented golf club with agolf club head configured with a gyroscope.

FIG. 9 is a flowchart diagram of a golf swing training process.

FIG. 10 is a flowchart diagram of a sample rate selection process.

DETAILED DESCRIPTION

In the following description of various example structures in accordancewith the invention, reference is made to the accompanying drawings,which form a part hereof, and in which are shown by way of illustrationvarious example golf club structures in accordance with the invention.Additionally, it is to be understood that other specific arrangements ofparts and structures may be utilized, and structural and functionalmodifications may be made without departing from the scope of thepresent invention. Also, while the terms “top,” “bottom,” “front,”“back,” “rear,” “side,” “underside,” “overhead,” and the like may beused in this specification to describe various example features andelements of the invention, these terms are used herein as a matter ofconvenience, e.g., based on the example orientations shown in thefigures and/or the orientations in typical use. Nothing in thisspecification should be construed as requiring a specific threedimensional or spatial orientation of structures in order to fall withinthe scope of this invention.

Additionally, while the embodiments described herein may be directedtoward golf and golf equipment, similar aspects may be applied and usedfor other types of sports and sports equipment.

General Description of Dynamic Sampling in a Golf Club According toVarious Examples

In general, aspects described herein relate to use of dynamic samplingin a golf analysis system for improved analysis performance. Forexample, dynamic sampling may be used to adjust sampling rates for oneor more analysis parameters based on a golf shot-type to be performed bya user or a current state of the user's swing. In some examples, dynamicsampling may be used to adjust one or more analysis parameters in orderto reduce power consumption by an analysis system.

FIG. 1 illustrates an example system and environment 100 in whichvarious aspects described herein may be used and implemented. Inparticular, system and environment 100 includes a golf club 102, a golfball 104, a personal computer 106, a mobile communication device 108, anetwork 110, and a server 112, wherein golf club 102 further includes asensor device 120. Golf club 102 may be a wood, iron, putter, hybrid, orspecialty club. In one embodiment, one or more golf swing, or golfequipment performance metrics may be determined by, and/or received by,personal computer 103 or mobile communication device 105. Mobilecommunication device 105 may comprise a tablet computer, a personal dataassistant (PDA), a smartphone, and/or combinations thereof. Personalcomputer 103 may include laptop computers or desktop computers. Devices103 and 105 may be connected to network 107 to a variety of otherdevices and destinations including server 109. Server 109 may beconfigured to collect data from various user devices as well as todistribute information such as fitness challenges, golf recommendations(e.g., equipment recommendations), product offers, and the like.Communication between two or more of golf club 102, golf ball 104,personal computer 106, and mobile communication device 108, orcombinations thereof, may be facilitated by wired or wirelesscommunication through network 110. Network 110 may be configured tocommunicate using, among others short range or long range technologies,and may include Wi-Fi, BLUETOOTH, infrared, satellite communications,cellular communications, or any wireless communication technology orprotocol. Alternatively, it will be readily apparent to those of skill,that network 110 may facilitate wired communication between one or moreof, golf club 102, golf ball 104, personal computer 106, mobilecommunication device 108, or combinations thereof. Wired communicationmay be facilitated by, among others Ethernet cabling, or any otherwiring technology configured communication analog or digital signals.Furthermore, network 110 may be, for example, a local area network(LAN), wide-area network (WAN), storage area network (SAN), theInternet, or any other network type, or combinations thereof

Generally, golf club 102 or golf ball 104, or combinations thereof, maycommunicate data to one or more of device 106, or device 108. Thecommunicated data may be, in one embodiment, raw data, or, in anotherembodiment, processed data representative of one or more golf swing, orgolf shot, performance metrics.

In one implementation, golf club 102 is configured with a sensor device120, wherein sensor device 120 includes one or more sensors. Thesesensors may further be configured to measure, sense, detect or otherwisedetermine one or more attributes (metrics) related to a golf swing,wherein such attributes may include, among others: speed, acceleration,orientation, location, or distance from an object, or combinationsthereof. Accordingly, the one or more sensors of sensor device 120 mayinclude, among other types of modules: accelerometers, gyroscopes,electromagnetic sensors, sound sensors (microphones), force (impact)sensors, global positioning system (GPS) sensors, or magnetic fieldsensors, or combinations thereof

In one or more arrangements, it may be desirable to dynamically adjustone or more sampling rates at which data is received from one or more ofthe aforementioned sensors. In this way, improved performance, and/orreduced power consumption during golf swing analysis may be achieved.

FIG. 2 is a schematic block diagram of sensor device 120 from FIG. 1.Specifically, sensor device 120 includes, among others, sensor 202,analysis processor 204, sampling rate processor 206, power supply 208, amemory 210, transceiver 212, and interface 214. As depicted in FIG. 2,components 202-214 of sensor device 120 may be configured as a singleintegrated circuit, however one of ordinary skill will realize thatcomponents 202-214 may alternatively be implemented on separateapplication-specific integrated circuits (ASICs). Alternatively,components 202-214 may be implemented using, among others:general-purpose integrated circuits, distributed hardware, or sharedhardware, or combinations thereof. Components 202-214 may also beconfigured such that one or more processes carried out by a respectivecomponent 202-214 are executed by, in one implementation, one or moreprocessor cores of a computational system, wherein the one or moreprocesses may be executed in parallel, or sequentially (in series). Inone example, analysis processor 204 and sampling rate processor 206 maycorrespond to a single processor.

Sensor 202 may be a single sensor, or represent a group of sensors of asame type, or of different types. In one configuration, sensor 202 maybe: an accelerometer, a gyroscope, an electromagnetic sensor, amicrophone, a force sensor, a magnetic field sensor, a GPS, aresistivity sensor, a wind speed and/or wind direction sensor, an imagesensor (camera), or combinations thereof In another configuration,sensor 202 may be configured to receive information from device 106 ordevice 108 related to the golf course conditions, or weather conditions,among others, wherein such information may be downloaded from theInternet. Sensor 202 may output an analog or a digital signalcorresponding to a physical quantity, wherein an analog output may be acontinuous voltage signal with a time-varying frequency and amplitudecorresponding to the frequency and magnitude of the physical quantity towhich the sensor is sensitive. Alternatively, a digital output may be apulse-width modulated signal, which has been transduced from acorresponding analog signal generated by a sensor.

Data output (e.g., golf shot performance metrics) from sensor 202 may bereceived by analysis processor 204, wherein analysis processor 204 isconfigured to execute one or more processes for determining, amongothers, one or more golf swing/golf shot categories, and/or one or moregolf swing/golf shot characteristics. Golf shot categories may includeexternal factors that may influence a golf swing or golf shot. Thesecategories include, among others, a lie of a golf ball on the golfcourse, and the weather conditions. Golf shot characteristics mayinclude metrics associated with the manner in which a user swings a golfclub to perform a golf shot. These characteristics may include, amongothers, backswing speed, and downswing speed, and the like.Subsequently, the one or more golf swing, or golf shot characteristicsand categories, as determined by an analysis processor 204, may becommunicated to a player using transceiver 212, or interface 214.Transceiver 212 may communicate through network 110 from FIG. 1, whereinthis communication may use one or more of Bluetooth, Wi-Fi, cellularcommunication, or any available wireless transmission protocol, orcombinations thereof. Furthermore, interface 214 may facilitate wiredcommunication of one or more processed performance metrics, and may be auniversal serial bus (USB) port, an Ethernet port, and the like.Interface 214 may also communicate one or more performance metrics to aplayer via one or more visual, audio, or haptic indicators, orcombination thereof, on golf club 102.

The one or more processes executed by analysis processor 204 may beexecuted according to non-transient, machine-executable instructionsstored in memory 210, wherein memory 210 may be a form of persistentmemory including one or more of: a hard disk drive (HDD), a solid statedrive (SSD), a read only memory (ROM), a register circuit, an opticaldisk (CD, DVD), a magnetic tape, or combinations thereof. Alternatively,memory 210 may be a form of volatile memory that is generally cleared bya reboot or power cycle operation of sensor device 120, and whereinmemory 210 may be a random access memory (RAM), among others.

According to some aspects, sensor device 120 is configured to bepositioned on, or within, a golf club 102. In another implementation,sensor device 120 is configured such that one or more components 202-214are physically separate from, but in communication with asensor-equipped golf club 102, and such that sensor device 120 isconfigured to be portable. In order to facilitate portability, sensordevice 120 may consume electrical energy from power supply 208. Powersupply 208 may be a form of stored chemical energy, such as a cell, orgroup of cells (commonly referred to as a battery). Alternatively oradditionally, power supply 208 may be implemented using one or more of acombination of other technologies, including solar cells, capacitors,which may be configured to store electrical energy harvested from themotion of device 120, a supply of electrical energy by “wireless”induction, or a wired supply of electrical energy from a mains outlet,such as a universal serial bus (USB 1.0/1.1/2.0/3.0, and the like).

Analysis processor 204 may sample data from sensor 202 at a firstsampling rate, wherein the first sampling rate may be a last-usedsampling rate by sensor device 120 prior to a reboot, sleep,hibernation, or power-down operation. In another example, the firstsampling rate may be a default sampling rate at which analysis processor204 samples data from sensor 202 upon initialization of analysisprocessor 204. In yet other examples, the first sampling rate iscommunicated to analysis processor 204 by sampling rate processor 206 asa default sampling rate. Sampling rate processor 206 may, in oneimplementation, receive data from sensor 202 and execute one or moreprocesses to select a sampling rate. In one implementation, samplingrate processor 206 may select a sampling rate at which an analysisprocessor 204 is to sample sensor data based on a golf swing categoryinto which the data received from sensor 202 is categorized.

In this way, analysis processor 204 may execute one or more processes tocategorize data received (e.g., at the first sampling rate) from sensor202 into one or more golf swing categories. Golf swing categories mayinclude, among others: a club type category, a distance-to-targetcategory, a lie-type, a course conditions category, a weather conditionscategory, and a wind direction category, among others. For example,analysis processor 204 may execute one or more processes to categorizedata received from sensor 202 into a golf swing category, wherein a clubtype may be communicated as a unique identification number from sensor202, associated with a golf club 102, to analysis processor 204.Alternatively or additionally, the club type may be manually specifiedby the user through the club 102 or a separate device (e.g., a mobilecommunication device). In some arrangements, a distance-to-targetcategory may be determined based on the club type. For example, a firsttype of driver may generally be configured to propel a golf ball a firstrange of distances while a second type of driver may generally beconfigured to propel a golf ball a second range of distances.Accordingly, the general range configuration of a club type may be usedto categorize the golf swing into a distance-to-target category.

In another example, analysis processor 204 may categorize received datainto a lie-type category indicative of a lie, or a localized positioningof a golf ball on the golf course. Categorization of a lie-type may bebased on, among others, an input from a player, or a resistivitymeasurement by a resistivity sensor on golf club 102 of the resistanceof the ground adjacent to a golf ball. One or more resistivitymeasurements may be taken when the golf club 102 is “grounded,” orpositioned with the head of the golf club 102 on the surface of the golfcourse prior to commencing a golf shot. In turn, memory 210 may storeone or more resistivity samples corresponding to different lie-types,e.g., a grass length indicative of a golf ball positioned on a fairway,or a grass length indicative of a golf ball positioned in light rough,and the like. Further, analysis processor 204 may execute one or moreprocesses to search memory 210 for a resistivity sample corresponding toone or more resistivity data points received from sensor 202.

According to other aspects, categorization of a lie-type may be based onone or more images of the localized position of a golf ball on the golfcourse. For example, a camera sensor may detect that the areasurrounding a golf ball on the golf course is white in color, wherein awhite color may be indicative of the golf ball being in a sand trap. Inaddition to color, contrast, brightness, color density and the like mayalso be used to analyze the lie-type based on an image.

Weather conditions may similarly be detected based on image orvideo-capture. Alternatively or additionally, weather conditions may bedetected based on temperature sensors, barometric pressure sensors, userinput, moisture detectors and the like and/or combinations thereof

Analysis processor 204 may categorize data received from sensor 202 intoa golf swing category based on a value of data received from sensor 202corresponding to one or more threshold values (or golf swing samples),wherein one or more threshold values may be stored in memory 210. Inanother example, sensor 202 may be an accelerometer, specifically athree-axis (x-, y-, and z-axis) accelerometer implemented as a singleintegrated circuit, or “chip”, wherein acceleration in one or more ofthe three axes is detected as a change in capacitance across a siliconstructure of a micro-electromechanical system (MEMS) device.Accordingly, a three-axis accelerometer may be used to resolve anacceleration in any direction in three-dimensional space. Furthermore,the accelerometer may output a signal indicative of one or moreaccelerations as a continuous voltage signal (analog signal) with a timevarying frequency and amplitude. Accordingly, one or more thresholdvalues stored in memory 210 may correspond to one or more accelerometerfrequency, and amplitude values, among others.

Sampling rate processor 206 may execute one or more processes to selecta sampling rate at which analysis processor 204 is to sample data fromsensor 202. Selection of a sampling rate by sampling rate processor 206,may, in one example, be based upon a golf swing category into which thedata from sensor 202 is categorized. In this way, sampling rateprocessor 206 may execute one or more processes to select a samplingrate corresponding to a golf swing category. For example, a table ofsampling rates corresponding to golf swing categories may be stored inmemory 210, such that sampling rate processor 206 may execute aniterative search through the stored table upon receipt of a golf swingcategory into which the data has been categorized. Various other search,or polling, method may be used by sampling rate processor 206 forsearching memory 210.

According to one arrangement, analysis processor 204 samples data fromsensor 202 at a first sampling rate to categorize data into a golf swingcategory, wherein the first sampling rate may be a last-used samplingrate, or a default sampling rate, among others. An iterative search,executed by sampling rate processor 206, may select and communicate asecond sampling rate to analysis processor 204, wherein analysisprocessor 204 may subsequently sample data from sensor 202 at the secondsampling rate. In another implementation, sampling rate processor 206receives data from sensor 202 before, or simultaneously to, analysisprocessor 204. In response, sampling rate processor 206, may execute oneor more processes to compare the data received from sensor 202 to one ormore threshold values, wherein one or more threshold values may bestored in a table structure in memory 210 with corresponding samplingrates. In one example, an average magnitude of a voltage signal from anaccelerometer, indicative of a magnitude of acceleration, may bereceived by sampling rate processor 206, and in response, compared toone or more stored threshold values. Sampling rate processor 206 maycompare received data to stored threshold values by iterativelysearching through a table of acceleration magnitude threshold valuesstored in memory 210.

Threshold values, stored in memory 210, may include amplitude thresholdvalues, frequency threshold values, amplitude change threshold values,or frequency change threshold values, among others. Accordingly, astored threshold value may correspond to one or more stored samplingrates.

According to some aspects, different portions of a golf swing may bedetected using different sample rates. For example, a moment of impactbetween a club head and the golf ball (and/or a short time surroundingthis moment) may be detected using a higher sampling rate to moreparticularly determine swing characteristics since the moment of impactmay provide important information about the golfer's performance. Incontrast, and in one example, a golfer's backswing might be detectedusing a different (e.g., lower) sampling rate, since the sensor data,during this time, might not exhibit as many rapid changes. To accountfor the different portions of the golf swing, different sampling ratesmay be defined. Upon detecting a particular portion of the golf swing, acorresponding sample rate may be used. These changes in sampling ratemay occur during the swing (e.g., after a swing has started and prior toimpact with a golf ball or prior to the golfer's follow through).

To detect the various portions of a golf swing, one or more triggers maybe defined. Triggers may correspond to threshold values of sensor data,time triggers, user input and the like. In one example, such triggersmay be determined based on a population of sample golf swings performedby the user. Sample golf swings may be captured and/or stored during atraining mode or during normal play as described in further detailbelow. In some examples, the user may specify that a golf swing is to beused in determining such triggers (e.g., to be added to the populationof sample golf swings from which triggers are to be determined). Inresponse, the analysis processor 204, for example, may store the golfswing into the population of sample data.

According to some arrangements, sampling rate processor 206 executes oneor more processes to select a sampling rate at which analysis processor204 samples data from sensor 202 based on one or more stored golf swingsamples stored from training data representative of a user's golf swing.Specifically, during an example training mode, analysis processor 204may execute processes requesting a user to perform, in oneimplementation, three “drive,” or “tee,” shots using a driver golf club,and the like. Analysis processor 204 may execute one or more processesto identify, from data received from sensor 202, one or morecharacteristics (otherwise referred to as metrics, patterns, or trends)of the user's drive golf shot, and store these characteristics as usergolf swing samples. These one or more characteristics may include, amongothers, a golf club lie angle, an impact angle, a golf club headorientation, a number of practice shots before hitting a golf ball, abackswing speed, a backswing time, a downswing speed, a downswing time,a follow-through distance, and a follow-through time, among others. Theone or more characteristics may be further broken down into one or moreoutputs from sensor 202, wherein an output from sensor 202 may be anamplitude value, or a frequency value, among others.

It is noted that a golf shot, or golf swing, may be broken down into abackswing, a downswing, a moment of impact with the golf ball and afollow-through, among others. A backswing may be a portion of a golfswing that includes raising a head of a golf club from a positionsubstantially at ground level, or addressing (positioning the golf clubclosely behind) a golf ball, to a position with the head of the golfclub spaced apart from the golf ball. A downswing may be a portion of agolf swing that includes moving the head of the golf club from aposition spaced apart from the golf ball, to a position at which thegolf club head is in contact with the golf ball, wherein upon contact ofthe golf club head and the golf ball, the golf club imparts kineticenergy onto the golf ball. A follow-through may be a portion of a golfswing including an impulse of a golf club head and a golf ball (theimpulse is the time during which the golf club head is in contact withthe golf ball), and the movement of the golf club head after impact withthe golf ball.

In some arrangements, analyzing a population of one or more sample golfswings to determine sampling rate change triggers may includedetermining an amount of time between various events. For example, anamount of time between swing initiation (e.g., start of a backswing) andgolf ball impact may be determined. This time may then be used totrigger activation of a higher sampling rate shortly before an expectedgolf ball impact in future shots. In another example, an amount of timebetween swing initiation and the start of the downswing may bedetermined to trigger activation of a higher sampling rate during thedownswing and into the moment of impact. In yet another example, athreshold (or trigger) velocity or acceleration after start of agolfer's downswing and prior to golf ball impact may be determined fromthe sample data population. According, upon detecting the threshold ortrigger velocity or acceleration (e.g., during the downswing), thesampling rate may be modified. In yet other examples, triggeringconditions (e.g., times, threshold velocities, accelerations or othermetrics) may be manually defined by the user.

The above noted sampling rate change triggers may be specific to agolfer, a club type, a club model, a golf course, a par-level for acourse, particular weather conditions (e.g., rain, sunny, windy), andthe like and/or combinations thereof. Accordingly, the population ofsample data may be categorized into different groups depending onvarious characteristics such as the aforementioned attributes. Eachgroup may then be evaluated separately to derive the various triggeringconditions

In some examples, when detecting and storing a golf swing as a sample,sampling rate processor 206 may instruct analysis processor 204 tosample golf swing data at a first sampling rate that may be a higher orupper (e.g., maximum) sampling rate. Analysis processor 204 may storeone or more data points, or golf swing samples, obtained from sensordata sampled at this upper sampling rate. Subsequently, sampling rateprocessor 206 may instruct analysis processor 204 to sample golf swingdata, for a same golf shot type, at one or more lower sampling rates.Analysis processor 204 may again execute one or more processes toidentify one or more characteristics, or golf swing samples, from sensordata sampled at lower sampling rates. Analysis processor 204 maysubsequently execute one or more processes to compare golf swingsamples, calculated at an upper sampling rate, to golf swing samplescalculated using lower sampling rates. In one embodiment, analysisprocessor 204 may identify a lower sampling rate at which a golf swingsample is substantially similar to a corresponding golf swing samplecalculated from data sampled at an upper sampling rate. A threshold ofsimilarity may be defined by maximum amount of deviation between metricsdetermined from the higher sampling rate sample and the lower samplingrate sample. In response, sampling rate processor 206 may store thelower sampling rate in combination with a golf swing sample. In anotherembodiment, analysis processor 204 identifies one or more sampling ratesthat are lower than an upper sampling rate, and corresponding to one ormore portions of a golf shot that are similar to one or more portions ofa golf shot calculated from data sampled at an upper sampling rate. Forexample, analysis processor 204 may execute, during a training mode, oneor more processes to compare a golf swing sample of a user's golf driveshot sampled from sensor data at an upper sampling rate, to one or moregolf swing samples of a golf drive shot sampled at one or more lowersampling rates. Analysis processor 204 may determine that the back swingportion of a golf drive shot, when sampled at an upper sampling rate, issimilar to the backswing portion of the golf drive shot when sampled ata lower sampling rate. In response, sampling rate processor 206 maystore the lower sampling rate in combination with the backswing portionof the golf drive shot, for a given user.

In some examples, sampling rate processor 206 may store one or moresampling rates corresponding to one or more characteristics of a golfshot using default, or predetermined sampling rate values. For example,sampling rate processor 206 may store one or more sampling ratescorresponding to one or more golf shot types based on default values forgolf swing characteristics, including: backswing speed, backswing time,downswing speed, downswing time, and follow-through time, among others.Accordingly, if data received from sensor 202 corresponds to one or moredefault golf swing characteristics, sampling rate processor 206instructs analysis processor 204 to sample data at a sampling ratecorresponding to the one or more default golf swing characteristics.

Sampling rates communicated by sampling rate processor 206 to analysisprocessor 204 may correspond to sampling rates in accordance with theNyquist sampling theorem (or Nyquist-Shannon sampling theorem), whichstates that in order to be able to accurately reproduce a signal, itshould be sampled at a frequency of at least double the highestfrequency present in the signal. For example, for acceleration datareceived from sensor 202 that includes a range of frequencies, rangingfrom 25 Hz to 100 Hz, the Nyquist sampling theorem states that in orderto accurately reproduce the received acceleration data, it should besampled at a sampling rate of at least 200 Hz. However, in otherimplementations, the sampling rate corresponding to stored thresholds inmemory 210 do not consider the Nyquist sampling theorem.

Sampling rate processor 206 may determine that data received from sensor202 corresponds to a threshold value if, among others, the received datais within a predetermined range of, closest to, but greater than, orequal to, a stored threshold value. Upon determination, by sampling rateprocessor 206, that data received from sensor 202 corresponds to astored threshold value, sampling rate processor 206 may communicate asampling rate, corresponding to the stored threshold value, to analysisprocessor 204 as a second sampling rate at which to sample data fromsensor 202.

According to one or more aspects, sampling rate processor 206, uponclassification of data from sensor 202 into one or more golf swingcategories, or upon determination of a golf swing characteristiccorresponding to the data received from sensor 202, may select one ormore new sensors. The one or more new sensors may be in addition tosensor 202, or may replace sensor 202, such that sampling rate processor206 executes one or more processes to determine one or more new sensorsthat are appropriate for collecting data related to a particular golfswing category or characteristic. For example, data received from asensor 202, embodied as an accelerometer, is classified into a golfswing category corresponding to a “drive” shot. In response, samplingrate processor 206 may select a new gyroscope sensor from which toreceive data instead of the accelerometer, wherein it is determined thata gyroscope sensor is more appropriate for collecting data related to a“drive” shot.

Advantageously, sampling rate processor 206 may reduce power consumptionfrom power supply 208, by, among others, analysis processor 204. In oneexample, a reduction in power consumption by analysis processor 204leads to an increase in time between recharges of a battery supplyingpower to a portable sensor device 120 in golf club 102. For example, ifanalysis processor 204 consumes a significant portion of the totalenergy used by sensor device 120, when sampling and analyzing data fromsensor 202 at a high, or an upper sampling rate, reduction in powerconsumption may significantly improve battery life. In a particularexample, analysis processor 204 may sample sensor data from sensor 202at an upper sampling rate of 50 Hz, and consume 95% of the totalelectrical energy of sensor device 120. It is further assumed that usinga sampling rate that is below a high, or upper sampling rate associatedwith analysis processor 204 could lead to significant reductions inpower consumption. For example, if the sampling rate of the analysisprocessor 204 is reduced to 24 Hz, the power consumption of sensordevice 120 may be reduced by 50%. Correspondingly, when the samplingrate of analysis processor 204 is reduced from 50 Hz to 24 Hz, thebattery life may be doubled.

FIG. 3 is a schematic block diagram of an exemplary embodiment of a golfclub 300 that includes a sensor device 120. In particular, golf club 300comprises a golf club head 302, a golf club shaft 304, a golf club grip306, a sensor 202, an analysis processor 204, a sampling rate processor206, a power supply 208, a memory 210, a transceiver 212, and aninterface 214. Golf club 300 from FIG. 3 may be similar to golf club 102from FIG. 1, such that a golf club 300 may communicate via network 110with a personal computer device 106, or a mobile communication device108. As depicted, golf club 300 is configured with sensor 202 positionedin a golf club head 302. However, it will be readily apparent to one ofordinary skill that sensor 202 may be positioned in golf club shaft 304,or golf club grip 306, among others. Similarly, although components 204to 214, as depicted, or positioned, in the golf club shaft 304, one ormore of components 204 to 214 may alternatively be positioned in golfclub grip 306 or golf club head 302, among others.

FIG. 4 is a schematic block diagram of an alternative implementation ofa golf swing analysis system 400. In particular, system 400 includes agolf club 401, a golf club head 402, a golf club shaft 404, a golf clubgrip 406, a sensor 202, a sensor 430, a power supply 208, a transceiver212, a network 110, a mobile communication device 108, an analysisprocessor 204 a sampling rate processor 206, a memory 210, and aninterface 214. FIG. 4 depicts golf club 401 with a first sensor 202, anda second sensor 430, wherein one or more of sensor 202, and sensor 430may represent one or more sensors of a same type, or of a differenttype. Furthermore, golf club 401 indicates that sensor 202, and sensor430 may be positioned within, or on, golf club 401 such that sensor 202and sensor 430 are spaced apart from one another. As depicted in FIG. 4,sensor 202 is positioned within golf club head 402, and sensor 430 ispositioned within golf club grip 406, however it will be readilyapparent one of ordinary skill that sensor 202 and sensor 430 may bepositioned within one or more of golf club head 402, golf club shaft404, or golf club grip 406.

As depicted, one or more of components 202 to 214 may communicate vianetwork 110. In particular, power supply 208 and transceiver 212 arepositioned within golf club shaft 404, and analysis processor 204,sampling rate processor 206, memory 210, and interface 214 arepositioned within the mobile communication device 108. Again, it will bereadily apparent to one of ordinary skill that system 400 may beconfigured such that one or more components 202 to 214 are in wirelesscommunication with one another via network 110, without departing fromthe spirit of the disclosure described in relation to components 202 to214, from FIG. 2.

FIG. 5 illustrates an example golf club head 500, configured withelectromagnetic sensors. In one embodiment, golf club 102 from FIG. 1may comprise a golf club head similar to golf club head 500. In anotherexample, one or more of sensors 502 a-502 e may be similar to sensor 202from FIG. 2. That is, golf club head 500 comprises electromagneticsensors, such as radio frequency sensors, or ultrasound sensors 502a-502 e. Sensors 502 a-502 e may be attached to or embedded in golf clubhead 500. In one embodiment, sensors 502 a-502 e are implemented withmicrostrip antennas. One skilled in the art will appreciate that one ormore of sensors 502 a-502 e may emit electromagnetic radiation orultrasound waves. Alternatively, electromagnetic radiation may beemitted by another source that may be attached to or embedded withingolf club head 500.

When electromagnetic sensors are used, club head speed may be determinedby measuring the Doppler frequency shift of waves reflected from a ball506. Golf club head 500, or another part of the golf club may include amodule for determining the Doppler frequency shift. Impact location maybe determined by measuring the phase shift of reflected signals fromball 506 just prior to impact, such as 15 cm prior to impact. Afrequency of 2 GHz may be used for a wavelength of 15 cm. The phaseshifts correspond to distances. The accuracy of the determination of theimpact location may be increased by using more sensors. In oneembodiment three sensors are used for determining impact location. Swingtempo may be determined by using the sensors as proximity sensors. Forexample, the sensors may be used to determine when golf club head 500 isin close proximity to ball 506 just prior to back swing and then beforeimpact. The time period between the two measurements corresponds to theswing tempo.

Ultrasound sensors may function in a similar manner. A number ofultrasound sensors, such as 2-5 may be attached to or embedded in thehead of a golf club. Club head speed may be determined by measuring afrequency shift in a signal reflected from a ball. For example, with atransducer of 40 kHz, a club head speed of 130 mph would result in a 70kHz reflection. A number of ultrasound sensors placed around the face ofthe club, such as two along each side and one on the top, may be used todetermine impact location. The time of flight of each signal just priorto impact corresponds to the distance between the ball and the sensor.The individual distances may be used to determine impact location.Ultrasound sensors may also function as proximity sensors to determineswing tempo in the manner described above.

In alternative examples, electromagnetic or ultrasound sensors may beplaced in or attached to a golfer's shoes to perform the functionssimilar to those described above. The sensors detect movement of theclub head which can be used to determine golf swing parameters.

FIG. 6 illustrates another example golf club head 600, configured withmagnetic field sensors 604. In one implementation, golf club 102 fromFIG. 1 may comprise a golf club head similar to golf club head 600. Inanother implementation, a magnetic field sensor 604 may be similar tosensor 202 from FIG. 2. The earth's magnetic field may also be used todetermine golf swing parameters. Magnetic field sensors may be attachedto or embedded within a golf club to detect components of the earth'smagnetic field at different club locations. As shown in FIG. 6, theearth's magnetic field represented by vector 602 is relatively constantin the vicinity of a golfer. A magnetic field sensor 604 resolvesmagnetic field vector 602 into three component vectors 606, 608 and 610.Magnetic field sensor 604 may be implemented with an anisotropicmagnetoresistive (AMR) device, a giant magnetoresistor (GMR) device orother suitable devices. As golf club head 600 moves, magnetic fieldvector 602 is resolved into component vectors 606, 608 and 610 such thatthe respective components have different magnitudes. The changingmagnitudes of the component vectors may then be used to determine golfswing parameters.

The club head face angle may be determined by first taking a referencemeasurement of the magnetic field before the back swing and then takinganother measurement of the magnetic field just prior to impact. Forexample, the magnitude of component vectors 606, 608 and 610 will havefirst values before the back swing and second values just prior toimpact. The different component vector values can then be used todetermine the face angle. If the magnetic field in the x-y plane isassumed to be 0.3 Gauss, the component X of the field with respect tocomponent vector 608 (x axis) is determined by X=0.3 cos θ and thecomponent Y of the field with respect to component vector 610 (y axis)is determined by Y=0.3 sin θ.

A 1 degree difference would cause a change in the magnitudes of vectorcomponents 608 and 610 as follows:ΔX=0.3(cos θ−cos (θ+1))ΔY=0.3(sin θ−sin (θ+1))

The smallest change that needs to be detected along each vectorcomponent may be determined by taking the derivative of each componentand determining were the derivative crosses the 0 axis.dX/dθ=−0.3 sin θ=0 at θ=0 degreesdY/dθ=0.3 cos θ=0 at θ=90 degrees

The highest resolution in the x-component is needed when the anglerotates from 0 to 1 degree and corresponds to 45.7 μG. The sameresolution is needed when the y-component rotates from 89 to 90 degrees.

Swing tempo may be determined by using vector component 606 (z axis) asa tilt sensor. A reference measurement of vector component 606 may berecorded before the back swing. The period required for the club head toreturn to a position such that the vector component 606 returns to themeasured reference value corresponds to the swing tempo. In analternative embodiment, velocity information may also be just todetermine impact time and the resulting swing tempo.

Several different measurements may be used to determine the swing path.FIG. 7 schematically depicts a swing path determination process. In oneimplementation, a swing path may be determined from velocity, time, andorientation measurements. For example, velocity and time informationmeasurements may be used to determine a first locus of points 702. Next,an orientation measurement may then be used to determine a firstlocation 704 along first locus of points 702. The process of identifyingclub locations may be repeated several times to determine a swing path706. In one embodiment, measurements are taken at least 1 kHz during aswing. Swing path 706 may be determined relative to a referenceorientation and impact location.

FIG. 8 illustrates yet another example golf club head 800, configuredwith a gyroscope. Golf club 102 from FIG. 1 may, in one implementation,comprise a golf club head similar to golf club head 800. Gyroscope 802may, in another implementation, be positioned within golf club head 800to measure one or more golf swing parameters. Gyroscope 802 may beimplemented with a micro-electromechanical system (MEMS) or other deviceor module capable of fitting within golf club head 804. A three-axisgyroscope may be used to increase accuracy.

Gyroscope 802 may be used to determine golf swing parameters by assumingthat the point of rotation is a golfer's shoulders. Club head velocitymay be determined by an accelerometer that is part of the same MEMS, anexternal accelerometer or some other device. For golf swing parameterdetermination purposes, in the proximity of a ball the movement of golfclub head 804 may be modeled as an object moving on the surface of asphere. The sphere has a radius equal to the length of the club plus thelength of the golfer's arms. In one embodiment, a standard radius of62.5 inches is used. In other embodiments, a golfer may provide his orher arm length and/or club length for more accurate determinations.

FIG. 9 is a flowchart diagram of example golf swing training process900. The golf swing training process 900 is initiated by analysisprocessor 204 from FIG. 2 at block 902. In one implementation, analysisprocessor 204 may initialize the golf swing training process 900 inresponse to, among others, an input from a user (golf player), or uponfirst initialization of sensor device 120, or combinations thereof.During golf swing training process 900, analysis processor 204 executesone or more processes for gathering and characterizing golf swing data.

Analysis processor 204, at block 904 of process 900, receives data fromone or more sensors during one or more “training” golf swings performedby a user. In one arrangement, analysis processor 204 communicates arequest to a user to perform a number of golf swings of a specific type.For example, analysis processor 204 may communicate, using one or moreof an audio, a visual, or a haptic cue, a request for a user to executethree successive “drive” shots. In other arrangements, analysisprocessor 204 may request that one or more training golf swings beperformed by the user in a specific way. For example, in addition toperforming three successive drives (drive shots), analysis processor 204may request that the user execute each drive shot as a straight shot,draw shot, or fade shot, and the like. In other embodiments, anddepending on the club-type being used by the player, analysis processor204 may request one or more training golf swings be performed of otherspecific types. For example, when using “long iron” golf club, analysisprocessor 204 may request one or more of a straight shot, a draw shot, afade shot, or a punch shot, and the like. In yet another example, whenusing a “wedge” golf club, the analysis processor 204 may request one ormore of a straight shot, a draw shot, a fade shot, or a flop shot, andthe like. In yet another embodiment, training golf swings may beperformed by a user by swinging a golf club and making impact with agolf ball, or swing a golf club without making impact with a golf ball.As noted herein, training golf swings may be golf swings performed in atraining mode as described, golf swings designated for storage in asample population and/or swings captured during normal play.Accordingly, the system might not necessarily instruct the user toperform certain types of swings. In one example, the system mayautomatically determine the type of swing or ask the user to identifythe type of swing post-swing.

During the one or more training golf swings performed by a user at block904, one or more sensor values (data points) are inputted by sensor 202,wherein these one or more sensor values are representative of the one ormore training golf swings performed. Analysis processor 204 receives thesensor data at block 906, wherein block 906 may include one or moreprocesses to check that the received data from sensor 202 corresponds toone or more general golf swing types. In this way, block 906 may comparethe received data to one or more generic golf swing data sets, andrequest that's the user repeats the training shots if the received datadoes not match the generic golf swing data sets to within a specifictolerance value. For example a generic golf swing data sets may includefour distinct sensor data patterns, including a pause prior tocommencement of the golf swing, one or more sensor values correspondingto a golf backswing, one or more sensor values corresponding to a golfdownswing, one or more sensor values indicative of a moment of impact ora point in time immediately prior to the moment of impact, and one ormore sensor values corresponding to a follow-through.

Analysis processor 204, at block 908, may execute one or more processesto extract one or more golf swing characteristics (patterns, thresholdvalues, and/or trends) from the received sensor data representative ofone or more training golf swings. This golf swing characterization maycompare multiple training golf swings of a same type to one another, andidentify one or more characteristics of the golf swings that are commonto each. In this way, analysis processor 204 may store one or morecharacteristics of a specific golf swing type associated with a givenuser. These one or more golf swing characteristics may include, amongothers, a number of practice shots before hitting a golf ball, abackswing speed, a backswing time, a downswing speed, a downswing time,a follow-through distance, and a follow-through time, othercharacteristics described herein among others. These characteristics maythen be used to define triggering conditions for dynamically modifyingthe sampling rate before, during and/or after a swing or portions of aswing.

In one example, sampling rate processor 206 instructs analysis processor204 to sample the training golf swings associated with blocks 904, 906,and 908, at an upper, or highest sampling rate. Subsequently, analysisprocessor 204, at block 910, may compare one or more identified swingcharacteristics, found using data sampled at a highest sampling rate, todata sampled at one or more lower sampling rates. If a swingcharacteristic identified from data sampled at an upper sampling rate isstill present in data sampled at a lower sampling rate, analysisprocessor may save, at block 914, the lower sampling rate in associationwith the identified swing characteristic.

In another example, and at block 912, analysis processor 204 comparesone or more identified golf swing characteristics to prototypical golfswing characteristics (prototypical golf swing samples). Prototypicalgolf swing characteristics may be stored as a data set in memory 210,wherein such a data sets may associate a prototypical golf swingcharacteristic with a sampling rate. In response analysis processor 204may store, at block 914, an identified swing characteristic inassociation with a sampling rate found from the prototypical golf swingcharacteristic data set.

FIG. 10 is a flowchart diagram of a sample rate selection process 1000,executed by sampling rate processor 206 from FIG. 2. In one example,process 1000 is initialized at block 1002 by the receipt of data. Datamay be received from, among others, sensor 202, or an outside source,such as weather data or golf course information data from the Internet.In another example, sensor device 120 may be in a sleep mode, orhibernation state until data is received at block 1002. In yet anotherexample, analysis processor 204 is in a sleep mode while sampling rateprocessor 206 executes one or more processes waiting for receipt of newdata block 1002. Upon receipt of data at block 1002, sampling rateprocessor 206 may execute one or more processes to “wake” analysisprocessor 204. In another embodiment, analysis processor 204 may be in asleep mode until sensor 202 outputs a signal representative of a golfclub being removed from a bag, or swung in a manner representative of apractice golf swing, and indicative of a user preparing to use the golfclub to take a golf shot. In this way, and while in a sleep orhibernation state, analysis processor 204 uses a low amount ofelectrical energy, and the overall power consumption by sensor device120 may be reduced by a comparatively significant amount.

Block 1004 represents one or more processes, executed by analysisprocessor 204, to characterize a golf shot from the data received atblock 1002. In particular, block 1004 characterizes golf shot data priorto a player addressing a golf ball, wherein addressing a golf ballrefers to positioning a golf club in close proximity to the golf balljust prior to commencing a golf shot. In this way, the data received atblock 1002 may be representative of, among others, a specific golf clubbeing selected out of a golf bag, or one or more practice swings, orcombinations thereof. At step 1004, analysis processor may execute oneor more processes to identify one or more golf shot categoriesincluding, among others, a golf club type, prevailing weather conditions(received, for example, by sensor device 120 through network 110),distance to pin (golf flagstick, or target position) information(received, for example, by sensor device through network 110 incombination with a GPS sensor 202), wind speed information (receivedfrom one or more of a wind speed sensor 202, or network 110, amongothers), golf course condition information (received, for example,through network 110, and including, among others, general golf coursecondition information including green speed information, and fairwaywater content information e.g. are “pick, clean, and place” rules ineffect, and the like), and player handicap information, or combinationsthereof, and the like. Upon identification of one or more golf shotcategories, sampling rate processor 206 may identify one or moresampling rates corresponding to the identified categories, and instructanalysis processor 204 to sample data from sensor 202 using thesenewly-identified sampling rates.

Analysis processor 204 receives further data, from one or more of sensor202 or network 110, at block 1008, wherein analysis processor 204samples data from sensor 202 at one or more sampling rates identified atblock 1006. Asked block 1010, analysis processor 204 may identify one ormore golf shot categories while addressing a golf ball. The one or moreidentified golf shot categories may include, among others, a lie type,or a golf shot type (identified based on the angle of a club face duringaddress of the golf ball), and the like. In a similar manner to block1006, sampling rate processor 206 selects one or more sampling rates atblock 1012 corresponding to the one or more categories of golf shotidentified at block 1010. In response, sampling rate processor 206instructs analysis processor 204 to receive data from, among others,sensor 202 at the newly-identified sampling rates from block 1012,wherein the one or more sampling rates are stored in memory 210 inassociation with the one or more golf shots categories.

Analysis processor 204 receives further data at block 1014, wherein thisreceived data is sampled at one or more sampling rates identified bysampling rate processor 206 at block 1012. At block 1016, one or moregolf swing characteristics are identified from the received data,wherein the golf swing characteristics represent real-time golf swingmotion. These golf swing characteristics may include, among others, aback swing speed, a back swing time, a den swing speed, a downswingtime, a follow-through distance, any follow-through time, among others.Sampling rate processor 206 may compare one or more of these identifiedgolf swing characteristics to one or more golf swing samples. In oneimplementation, the one or more golf swing samples may have been savedin combination with one or more sampling rates during a training mode,wherein the training mode is executed by analysis processor 204. Inresponse, sampling rate processor 206 selects, at block 1018, one ormore sampling rates corresponding to the one or more identified golfswing characteristics. In response, sampling rate processor 206 executesone or more processes to instruct analysis processor 204 to samplefuture incoming data received from, among others, sensor 202, at thenewly-selected sampling rates.

In this way, sample rate selection process 1000 may dynamically adjust asampling rate of analysis processor 204 such that data from sensor 202is sampled at a rate sufficient for capturing data representative of thegolf swing, or part thereof. Furthermore, sampling rate processor 206may adjust the sampling rate used by analysis processor 204 multipletimes before, during, and after golf swing, among others e.g. blocks1006, 1012, and 1018. Additionally, the adjustment of a sampling rate,by sampling rate processor 206 may achieve an overall reduction in powerconsumption from power supply 208, by sensor device 120.

The present disclosure is described above and in the accompanyingdrawings with reference to a variety of example structures, features,elements, and combinations of structures, features, and elements. Thepurpose served by the disclosure, however, is to provide examples of thevarious features and concepts related to the disclosure, not to limitthe scope of the disclosure. One skilled in the relevant art willrecognize that numerous variations and modifications may be made to theembodiments described above without departing from the scope of thepresent disclosure, as defined by the appended claims. For example,aspects described herein may be applied to a variety of sports andsporting equipment. In one or more arrangements, devices and processeshaving dynamic sampling rate properties described herein may be used todetermine sports actions performed with a baseball bat, lacrosse stick,hockey stick, boxing gloves and the like. In one example, a samplingrate may be increased (or decreased) for a hockey stick based sensorupon determining that a player is about to take a shot. Whether theplayer is about to take a shot may be determined in a variety of waysincluding detecting acceleration in a forward direction by a sensordisposed in the hockey stick. In another example, a sampling rate may beincreased (or decreased) when a player is beginning to take a swing witha baseball bat to his an incoming baseball or immediately prior to amoment of impact with a baseball.

We claim:
 1. An item of sports equipment, comprising: a processor; asensor configured to capture sensor data related to at least one metricof a swing; a transceiver configured to receive transceiver data relatedto a location of a ball, and prevailing conditions; and a non-transitorycomputer-readable medium comprising computer-executable instructionsthat when executed by the processor is configured to cause the item ofsports equipment to perform: receiving the sensor data from the sensor;sampling the received sensor data at a first sampling rate; receivingthe transceiver data from the transceiver; identifying, from the sensordata, an orientation of the item of sports equipment indicating a shottype; classifying the sensor and transceiver data into one of aplurality of swing categories, based upon the identified orientation ofthe item of sports equipment indicating a shot type; and based upon atleast the classified swing category, selecting a second sampling rate,by a sampling rate processor, for sampling sensor data from the sensor.2. The item of sports equipment of claim 1, wherein the non-transitorycomputer-readable medium further comprises instructions that whenexecuted further cause the item of sports equipment to perform:comparing a first value of the sensor data obtained from the sensorduring operation of the processor at a first sampling rate to aplurality of threshold values; determining that the first value of thesensor data corresponds to a first threshold value within the pluralityof threshold values; and wherein the selection of the second samplingrate is based upon both the correspondence of the first value of thesensor data to the first threshold value and the classified swingcategory.
 3. The item of sports equipment of claim 1, wherein the itemof sports equipment is a baseball bat, a lacrosse stick, or a hockeystick.
 4. The item of sports equipment of claim 1, wherein thetransceiver and sensor data is classified into a swing category based ona selected type of item of sports equipment.
 5. The item of sportsequipment of claim 1, wherein the transceiver data includes locationinformation, and is classified into a swing category based on a distanceto a target.
 6. The item of sports equipment of claim 1, wherein thesensor and transceiver data is classified into the one of the pluralityof swing categories based on a wind speed and a wind direction.
 7. Theitem of sports equipment of claim 1, wherein the sensor is selected froma group comprising at least one of: an accelerometer, a force sensor, agyroscope, a magnetic field sensor, an electromagnetic sensor, amicrophone, a GPS sensor, a wind speed and direction sensor, and aresistivity sensor.
 8. The item of sports equipment of claim 1, whereinthe at least one metric of the swing includes a swing speed, or a swingacceleration.
 9. The item of sports equipment of claim 1, wherein thefirst sampling rate is a default sampling rate.
 10. The item of sportsequipment of claim 1, wherein the first sampling rate is a last-usedsampling rate.
 11. An item of sports equipment comprising: a firstsensor configured to capture data related to at least one metric of aswing; a second sensor configured to capture data related to prevailingconditions; a processor; and a non-transitory machine readable mediumcomprising instructions that when executed by the processor cause theitem of sports equipment to: receive first data from the first sensor;sample the received first data at a first sampling rate; receive seconddata from the second sensor; identify, from the sensor data, anorientation of the item of sports equipment indicating a shot type;classify the first and second data into one of a plurality of swingcategories, based upon the identified orientation of the item of sportsequipment indicating a shot type; and select, based on theclassification of the first and second data into the one of a pluralityof swing categories, a second sampling rate for sampling data from thefirst sensor.
 12. The item of sports equipment of claim 11, wherein thesecond sampling rate is lower than the first sampling rate.
 13. The itemof sports equipment of claim 12, wherein the at least one metric of theswing is identifiable in data received at the second sampling rate fromthe first sensor.
 14. The item of sports equipment of claim 11, whereinthe at least one metric of the swing includes a swing speed, or a swingacceleration.
 15. The item of sports equipment of claim 11, wherein thefirst sensor is an accelerometer, a force sensor, a gyroscope, amagnetic field sensor, an electromagnetic sensor, a microphone, a GPSsensor, a wind speed and direction sensor, or a resistivity sensor. 16.A self-contained instrumented item of sports equipment, comprising: ashaft; a first sensor, configured to capture swing data related to atleast one metric of a swing; a second sensor configured to capturecondition data related to prevailing weather conditions; a sampling rateprocessor; and an analysis processor, wherein the analysis processor isconfigured to identify, from the swing data, an orientation of the itemof sports equipment indicating a shot type; wherein the analysisprocessor is configured to classify the swing data and condition datainto a swing category based upon the identified orientation of the itemof sports equipment indicating a shot type, wherein the sampling rateprocessor is configured to select a first sampling rate and a secondsampling rate at which the first sensor captures swing data, based uponthe swing category, and wherein the analysis processor is configured tocompare the swing data sampled at the first sampling rate and the secondsampling rate.
 17. The instrumented item of sports equipment of claim16, wherein the swing category is classified based on a selected item ofsports equipment, or a distance to target.
 18. The instrumented item ofsports equipment of claim 16, wherein the second sampling rate is lessthan the first sampling rate.
 19. The instrumented item of sportsequipment of claim 18, wherein the at least one metric of the swing isidentifiable in the swing data received at the second sampling rate. 20.The instrumented item of sports equipment of claim 16, wherein the itemof sports equipment is a baseball bat, a lacrosse stick, or a hockeystick.